This lesson introduces some of the common vocabulary when studying statistics. Site: http://mathispower4u.com
From playlist Introduction to Statistics
This lecturelet will introduce you to the series on statistical analyses of time-frequency data. For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
From playlist OLD ANTS #8) Statistics
(PP 4.1) Expectation for discrete random variables
(0:00) Definition of expectation for discrete r.v.s. (4:17) Well-defined expectation. (8:15) E(X) may exist and be infinite. (10:58) E(X) might fail to exist. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
Introduction to Estimation Theory
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. General notion of estimating a parameter and measures of estimation quality including bias, variance, and mean-squared error.
From playlist Estimation and Detection Theory
From playlist STAT 501
The Most Powerful Tool Based Entirely On Randomness
We see the effects of randomness all around us on a day to day basis. In this video we’ll be discussing a couple of different techniques that scientists use to understand randomness, as well as how we can harness its power. Basically, we'll study the mathematics of randomness. The branch
From playlist Classical Physics by Parth G
Definitions of random variable, Outcome versus Event, Mutually exclusive events, and Exhaustive events. For more financial risk videos, visit our website! http://www.bionicturtle.com
From playlist Statistics: Introduction
This video explains how to determine mean, median and mode. It also provided examples. http://mathispower4u.yolasite.com/
From playlist Statistics: Describing Data
Hypothesis testing in statistics
Hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter or a population probability distribution. First, a tentative assumption is made about the parameter or distribution. This assumption is called the null hypoth
From playlist Statistics
Check Your Assumptions – The Test Assumptions of Statistical Testing (8-12)
You know what happens when you assume? If your assumptions are wrong, it prevents you from looking at the world accurately. Parametric inferential statistics are built on certain assumptions about the data. And if those assumptions are violated, the conclusions based on those assumptions a
From playlist WK8 Statistical Hypothesis Testing (NHST) - Online Statistics for the Flipped Classroom
Parametric vs. nonparametric statistics
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #5) Permutation-based statistics
Table of Contents: 00:50 - Lecture structure Two Proportions 01:11 - Checking assumptions 02:50 - Computing the standard error by hand 03:59 - Example: Computing the standard error for a confidence interval 06:22 - Example: Computing the standard error for a hypothesis test 08
From playlist STAT 200 Video Lectures
Check Your Assumptions for Hypothesis Testing: An Introduction (Week 16A)
If our assumptions are wrong, then any conclusions drawn from those assumptions will likely be wrong. Wrapping up Hypothesis Testing, we dive deeper into some ideas that are not in the textbook but are vital for real world analysis. First, we explore what it means to check the assumptions
From playlist Basic Business Statistics (QBA 237 - Missouri State University)
The Assumption of NORMALITY in Parametric Hypothesis Tests (16-6)
Parametric statistical tests require normality, which does not mean what many people think it means. I explain the true meaning of the assumption of normality, using Stats Blocks, and how to test this assumption with graphs or tests, such as Kolmogorov-Smirnov Test. The Central Limit Theor
From playlist Assumptions, Significance, & Effect Size Wrap-Up (WK 16 - QBA 237)
Check Your ASSUMPTIONS for Parametric Hypothesis Tests (16-2)
All parametric statistical tests require that the population have certain characteristics. If the data do not meet those assumptions, then any conclusions drawn from the test may be wrong. Some assumptions can be addressed when designing your research and others can only be checked once yo
From playlist Assumptions, Significance, & Effect Size Wrap-Up (WK 16 - QBA 237)
From playlist STAT 200 Video Lectures
Jean-Michel Zakoïan: Testing the existence of moments for GARCH-type processes
It is generally admitted that financial time series have heavy tailed marginal distributions. When time series models are fitted on such data, the non-existence of appropriate moments may invalidate standard statistical tools used for inference. Moreover, the existence of moments can be cr
From playlist Probability and Statistics
One Sample t Test Framework with Gardening Example (15-3)
The one sample z test is a parametric procedure that tests whether a single sample mean is significantly different than a population mean when the standard deviation of the population (σ) is UNKNOWN. We will learn the research design for the IV and DV, the assumptions for the test, how to
From playlist Single-Sample Hypothesis Tests (z, t, & p) - WK 15 QBA 237
(PP 6.3) Gaussian coordinates does not imply (multivariate) Gaussian
An example illustrating the fact that a vector of Gaussian random variables is not necessarily (multivariate) Gaussian.
From playlist Probability Theory
Welcome to my introductory series on Data Science! In this video, I explain how to talk about data in general, the difference between qualitative and quantitative data and finally, how to describe data succinctly by using summary statistics. Link to my notes on Introduction to Data Scien
From playlist Introduction to Data Science - Foundations